Return to search

Novel Treatments for Multi-phase Flow Prediction Inspired By Kinetic Theory

This study entails an investigation of a novel moment closure, originally constructed for rarefied-gas prediction, to the modelling of inert, dilute, disperse, particle flows. Such flows are important in many engineering situations. As one example, in internal-combustion engines, fuel is often injected as a spray of tiny droplets and, during combustion, a cloud of tiny soot particles can be formed. These particle phases are often difficult to model, especially when particles display a range of velocities at each location in space. Lagrangian methods are often too costly and many Eulerian field-based methods suffer from model deficiencies and mathematical artifacts. Often, Eulerian formulations assume that all particles at a location and time have the same velocity. This assumption leads to nonphysical
results, including an inability to predict particle paths crossing and a limited number of boundary conditions that can be applied.
The typical multi-phase situation of many particles is, in many ways, similar to that
of a gas compressed of a huge number of atoms or molecules. It is therefore expected that powerful techniques from the kinetic theory of gases could be applied. This work explores the advantages of using a modern fourteen-moment model, originally derived for rarefied gases, to predict multi-phase flows. Details regarding the derivation, the mathematical structure, and physical behaviour of the resulting model are explained. Finally, a numerical implementation is presented and results for several flow problems that are designed to demonstrate the fundamental behaviour of the models are presented. Comparisons are made with other classical models.

Identiferoai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/34924
Date January 2016
CreatorsBen Dhia, Zakaria
ContributorsMcDonald, James
PublisherUniversité d'Ottawa / University of Ottawa
Source SetsUniversité d’Ottawa
LanguageEnglish
Detected LanguageEnglish
TypeThesis

Page generated in 0.0022 seconds